Get the data

To run this code, first set your credentials in the environment variables SB_USER and SB_PASS. Or, change this to use the StatsBomb free data!

matches <- StatsBombR::get.matches(
  Sys.getenv('SB_USER'),
  Sys.getenv('SB_PASS'),
  season_id = params$season_id,
  competition_id = params$comp_id
)

team_name <- matches[matches$home_team.home_team_id == params$team_id,]$home_team.home_team_name[1]

# slice to the last n games by the selected team
match_ids <- matches |>
  dplyr::filter(
    home_team.home_team_id == params$team_id | away_team.away_team_id == params$team_id
  ) |>
  dplyr::mutate(match_date = as.Date(match_date)) |>
  dplyr::arrange(dplyr::desc(match_date)) |>
  dplyr::slice(1:params$n_games) |>
  dplyr::pull(match_id)

# pull out the match events for all the games
match_events <- StatsBombR::allevents(
  Sys.getenv('SB_USER'),
  Sys.getenv('SB_PASS'),
  matches = match_ids
) |> 
    StatsBombR::allclean()

## Time difference of 8.25471 secs

Goalkicks

goalkicks <- match_events |> 
    dplyr::filter(pass.type.name == 'Goal Kick', team.id == params$team_id) |> 
    dplyr::mutate(
        success = !is.na(pass.outcome.id)
    ) |> 
    dplyr::select(
        x = location.x, y = location.y, end.x = pass.end_location.x, end.y = pass.end_location.y, success
    )

create_goalkick_chart(goalkicks, colour = params$colour) +
    ggplot2::ggtitle(
        label = paste0(team_name, ' Goalkick Zones'),
        subtitle = paste0(
            'Last ', params$n_games, ' Games'
        )
    )

Team Passing In First Third

for(pass.num in 1:3) {
    passes <- match_events |> 
        dplyr::filter(type.name == 'Pass') |> 
        dplyr::group_by(match_id, possession) |> 
        dplyr::mutate(
            action_number = dplyr::row_number()
        ) |> 
        # just passes in the first third
        dplyr::filter(
            location.x[1] <= 40
        ) |> 
        dplyr::ungroup() |> 
        dplyr::filter(
            team.id == params$team_id, 
            action_number == pass.num,
            !is.na(pass.pass_cluster_id)
        ) |> 
        dplyr::select(
            x = location.x, 
            y = location.y,
            end.x = pass.end_location.x,
            end.y = pass.end_location.y, 
            cluster = pass.pass_cluster_id
        )

    plot <- create_pass_clusters_chart(passes, colour = params$colour) +
        ggplot2::ggtitle(
            label = paste0(
                team_name, 
                ' First Third ', 
                toOrdinal::toOrdinal(pass.num),
                ' Passes'
            ),
            subtitle = paste0(
                'Last ', params$n_games, ' Games'
            )
        )

    print(plot)
}

Midfield Passes

passes <- match_events |> 
    dplyr::filter(
        type.name == 'Pass',
        team.id == params$team_id, 
        !is.na(pass.pass_cluster_id),
        position.id %in% c(9, 10, 11, 13, 14, 15)
    ) |> 
    dplyr::select(
        player.name,
        x = location.x, 
        y = location.y,
        end.x = pass.end_location.x,
        end.y = pass.end_location.y, 
        cluster = pass.pass_cluster_id
    )

for(player in unique(passes$player.name)) {
    plot <- passes |> 
        dplyr::filter(player.name == player) |> 
        create_pass_clusters_chart(n.clust = 5, colour = params$colour) +
        ggplot2::ggtitle(
            label = paste0(
                player, ' Most Common Passes'
            ),
            subtitle = paste0(
                'Last ', params$n_games, ' Games'
            )
        )

    print(plot)
}